7 citations · 15 across the 8 of their papers we have counts for
8 papers
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Shasha Guo, Lizi Liao, Jing Zhang +3
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the…
Open-World Semi-Supervised Learning for Node Classification
Yanling Wang, Jing Zhang, Lingxi Zhang +5
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
A Survey on Neural Question Generation: Methods, Applications, and Prospects
Shasha Guo, Lizi Liao, Cuiping Li +1
In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questio…
Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch
Pan Du, Suyun Zhao, Zisen Sheng +2
Semi-Supervised Learning (SSL) under class distribution mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown categories unseen in the labele…
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Haoyang Li, Jing Zhang, Cuiping Li +1
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of p…